The proposed work develops further the statistical energy landscape approach to protein folding dynamics and structure prediction. The specific aims are: 1) to develop fundamental statistical mechanical theories for surveying the energy landscapes of landscapes during folding; 2) to predict and interpret the structural aspects of the early events in protein folding; 3) to understand the microscopic origin of kinetic barriers to folding on long time scales and of protein metastability; 4) to further develop rigorously based statistical algorithms for the prediction of protein tertiary structure. Energy landscape theory provides mathematical techniques for characterizing in probabilistic terms the energies of the ensembles of partially folded protein configurations and the dynamics of interconverting between them. Both analytical and computer simulation approaches are proposed that will provide quantitative estimates for the role individual amino acids play both in guiding the protein to its native state and in impeding that flow. Changes in folding kinetics affect protein trafficking and are thought to be involved in the pathogenesis of many diseases including Alzheimer's disease, type II diabetes and cystic fibrosis. The improved ability to predict accurate three dimensional protein structures from sequence will be of great value in generally making use of data obtained from both human and bacterial genome sequencing projects.